import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    MODEL_MAPPING,
			    SegformerForImageClassification,
			    SegformerForSemanticSegmentation,
			    SegformerModel,
			)
			from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import SegformerImageProcessor
class 	_lowerCAmelCase		(  lowerCamelCase__					):







							def 					snake_case	(					self  :     Union[str, Any]		):
										lowerCamelCase    :Union[str, Any]    	=  self.config_class(**self.inputs_dict		)
										self.parent.assertTrue(hasattr(__snake_case						,       '''hidden_sizes'''		)		)
										self.parent.assertTrue(hasattr(__snake_case						,       '''num_attention_heads'''		)		)
										self.parent.assertTrue(hasattr(__snake_case						,       '''num_encoder_blocks'''		)		)


class 	_lowerCAmelCase		:







							def __init__(					self  :     str						,       __snake_case  :     List[str]						,       __snake_case  :     Union[str, Any]=13						,       __snake_case  :     Optional[Any]=64						,       __snake_case  :     int=3						,       __snake_case  :     str=4						,       __snake_case  :     Optional[int]=[2, 2, 2, 2]						,       __snake_case  :     Optional[Any]=[8, 4, 2, 1]						,       __snake_case  :     int=[16, 32, 64, 128]						,       __snake_case  :     Tuple=[1, 4, 8, 16]						,       __snake_case  :     Union[str, Any]=[1, 2, 4, 8]						,       __snake_case  :     Tuple=True						,       __snake_case  :     Tuple=True						,       __snake_case  :     List[str]="gelu"						,       __snake_case  :     Union[str, Any]=0.1						,       __snake_case  :     List[str]=0.1						,       __snake_case  :     int=0.0_2						,       __snake_case  :     List[str]=3						,       __snake_case  :     Tuple=None						,       ):
										lowerCamelCase    :Optional[Any]    	=  parent
										lowerCamelCase    :List[str]    	=  batch_size
										lowerCamelCase    :Dict    	=  image_size
										lowerCamelCase    :Optional[int]    	=  num_channels
										lowerCamelCase    :Optional[Any]    	=  num_encoder_blocks
										lowerCamelCase    :Dict    	=  sr_ratios
										lowerCamelCase    :Tuple    	=  depths
										lowerCamelCase    :Optional[Any]    	=  hidden_sizes
										lowerCamelCase    :str    	=  downsampling_rates
										lowerCamelCase    :List[Any]    	=  num_attention_heads
										lowerCamelCase    :List[Any]    	=  is_training
										lowerCamelCase    :Tuple    	=  use_labels
										lowerCamelCase    :Tuple    	=  hidden_act
										lowerCamelCase    :int    	=  hidden_dropout_prob
										lowerCamelCase    :Dict    	=  attention_probs_dropout_prob
										lowerCamelCase    :Optional[Any]    	=  initializer_range
										lowerCamelCase    :List[str]    	=  num_labels
										lowerCamelCase    :Union[str, Any]    	=  scope







							def 					snake_case	(					self  :     Optional[int]		):
										lowerCamelCase    :Optional[int]    	=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

										lowerCamelCase    :List[Any]    	=  None
										if self.use_labels:
													lowerCamelCase    :List[str]    	=  ids_tensor([self.batch_size, self.image_size, self.image_size]						,       self.num_labels		)

										lowerCamelCase    :List[Any]    	=  self.get_config()
										return config, pixel_values, labels







							def 					snake_case	(					self  :     Optional[int]		):
										return SegformerConfig(
										    image_size=self.image_size						,       num_channels=self.num_channels						,       num_encoder_blocks=self.num_encoder_blocks						,       depths=self.depths						,       hidden_sizes=self.hidden_sizes						,       num_attention_heads=self.num_attention_heads						,       hidden_act=self.hidden_act						,       hidden_dropout_prob=self.hidden_dropout_prob						,       attention_probs_dropout_prob=self.attention_probs_dropout_prob						,       initializer_range=self.initializer_range						,       )







							def 					snake_case	(					self  :     int						,       __snake_case  :     Optional[Any]						,       __snake_case  :     Any						,       __snake_case  :     List[Any]		):
										lowerCamelCase    :Union[str, Any]    	=  SegformerModel(config=__snake_case		)
										model.to(__snake_case		)
										model.eval()
										lowerCamelCase    :Optional[int]    	=  model(__snake_case		)
										lowerCamelCase    :Optional[Any]    	=  self.image_size // (self.downsampling_rates[-1] * 2)
										self.parent.assertEqual(
										    result.last_hidden_state.shape						,       (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)		)







							def 					snake_case	(					self  :     Optional[int]						,       __snake_case  :     List[Any]						,       __snake_case  :     Any						,       __snake_case  :     List[Any]		):
										lowerCamelCase    :Optional[Any]    	=  self.num_labels
										lowerCamelCase    :str    	=  SegformerForSemanticSegmentation(__snake_case		)
										model.to(__snake_case		)
										model.eval()
										lowerCamelCase    :int    	=  model(__snake_case		)
										self.parent.assertEqual(
										    result.logits.shape						,       (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)		)
										lowerCamelCase    :List[str]    	=  model(__snake_case						,       labels=__snake_case		)
										self.parent.assertEqual(
										    result.logits.shape						,       (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)		)
										self.parent.assertGreater(result.loss						,       0.0		)







							def 					snake_case	(					self  :     Any						,       __snake_case  :     Union[str, Any]						,       __snake_case  :     int						,       __snake_case  :     Any		):
										lowerCamelCase    :Dict    	=  1
										lowerCamelCase    :Dict    	=  SegformerForSemanticSegmentation(config=__snake_case		)
										model.to(__snake_case		)
										model.eval()
										lowerCamelCase    :List[str]    	=  torch.randint(0						,       1						,       (self.batch_size, self.image_size, self.image_size)		).to(__snake_case		)
										lowerCamelCase    :List[Any]    	=  model(__snake_case						,       labels=__snake_case		)
										self.parent.assertGreater(result.loss						,       0.0		)







							def 					snake_case	(					self  :     List[str]		):
										lowerCamelCase    :List[str]    	=  self.prepare_config_and_inputs()
										lowerCamelCase    :Tuple    	=  config_and_inputs
										lowerCamelCase    :Dict    	=  {'pixel_values': pixel_values}
										return config, inputs_dict


@require_torch
class 	_lowerCAmelCase		(  lowerCamelCase__   , lowerCamelCase__   , unittest.TestCase					):
							_UpperCAmelCase         =  (
							    (
							        SegformerModel,
							        SegformerForSemanticSegmentation,
							        SegformerForImageClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							_UpperCAmelCase         =  (
							    {
							        'feature-extraction': SegformerModel,
							        'image-classification': SegformerForImageClassification,
							        'image-segmentation': SegformerForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase         =  True
							_UpperCAmelCase         =  False
							_UpperCAmelCase         =  False
							_UpperCAmelCase         =  False







							def 					snake_case	(					self  :     Optional[int]		):
										lowerCamelCase    :Optional[Any]    	=  SegformerModelTester(self		)
										lowerCamelCase    :List[Any]    	=  SegformerConfigTester(self						,       config_class=__snake_case		)







							def 					snake_case	(					self  :     Dict		):
										self.config_tester.run_common_tests()







							def 					snake_case	(					self  :     str		):
										lowerCamelCase    :Dict    	=  self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__snake_case		)







							def 					snake_case	(					self  :     int		):
										lowerCamelCase    :Any    	=  self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_binary_image_segmentation(*__snake_case		)







							def 					snake_case	(					self  :     int		):
										lowerCamelCase    :Dict    	=  self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_segmentation(*__snake_case		)







							@unittest.skip('''SegFormer does not use inputs_embeds'''		)
							def 					snake_case	(					self  :     List[str]		):
										pass







							@unittest.skip('''SegFormer does not have get_input_embeddings method and get_output_embeddings methods'''		)
							def 					snake_case	(					self  :     str		):
										pass







							def 					snake_case	(					self  :     Optional[int]		):
										lowerCamelCase    :str    	=  self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase    :Union[str, Any]    	=  model_class(__snake_case		)
													lowerCamelCase    :Optional[Any]    	=  inspect.signature(model.forward		)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowerCamelCase    :Any    	=  [*signature.parameters.keys()]

													lowerCamelCase    :Optional[int]    	=  ['pixel_values']
													self.assertListEqual(arg_names[:1]						,       __snake_case		)







							def 					snake_case	(					self  :     str		):
										lowerCamelCase    :List[Any]    	=  self.model_tester.prepare_config_and_inputs_for_common()
										lowerCamelCase    :Any    	=  True

										for model_class in self.all_model_classes:
													lowerCamelCase    :Tuple    	=  True
													lowerCamelCase    :Tuple    	=  False
													lowerCamelCase    :str    	=  True
													lowerCamelCase    :Optional[int]    	=  model_class(__snake_case		)
													model.to(__snake_case		)
													model.eval()
													with torch.no_grad():
																lowerCamelCase    :Dict    	=  model(**self._prepare_for_class(__snake_case						,       __snake_case		)		)
													lowerCamelCase    :Dict    	=  outputs.attentions

													lowerCamelCase    :Union[str, Any]    	=  sum(self.model_tester.depths		)
													self.assertEqual(len(__snake_case		)						,       __snake_case		)

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													lowerCamelCase    :List[Any]    	=  True
													lowerCamelCase    :str    	=  model_class(__snake_case		)
													model.to(__snake_case		)
													model.eval()
													with torch.no_grad():
																lowerCamelCase    :Optional[int]    	=  model(**self._prepare_for_class(__snake_case						,       __snake_case		)		)
													lowerCamelCase    :Union[str, Any]    	=  outputs.attentions

													self.assertEqual(len(__snake_case		)						,       __snake_case		)

													# verify the first attentions (first block, first layer)
													lowerCamelCase    :str    	=  (self.model_tester.image_size // 4) ** 2
													lowerCamelCase    :Union[str, Any]    	=  (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(attentions[0].shape[-3:]		)						,       [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]						,       )

													# verify the last attentions (last block, last layer)
													lowerCamelCase    :List[Any]    	=  (self.model_tester.image_size // 32) ** 2
													lowerCamelCase    :List[str]    	=  (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
													self.assertListEqual(
													    list(attentions[-1].shape[-3:]		)						,       [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]						,       )
													lowerCamelCase    :List[Any]    	=  len(__snake_case		)

													# Check attention is always last and order is fine
													lowerCamelCase    :Optional[int]    	=  True
													lowerCamelCase    :str    	=  True
													lowerCamelCase    :Optional[int]    	=  model_class(__snake_case		)
													model.to(__snake_case		)
													model.eval()
													with torch.no_grad():
																lowerCamelCase    :Dict    	=  model(**self._prepare_for_class(__snake_case						,       __snake_case		)		)

													self.assertEqual(out_len + 1						,       len(__snake_case		)		)

													lowerCamelCase    :Dict    	=  outputs.attentions

													self.assertEqual(len(__snake_case		)						,       __snake_case		)
													# verify the first attentions (first block, first layer)
													lowerCamelCase    :Tuple    	=  (self.model_tester.image_size // 4) ** 2
													lowerCamelCase    :Optional[Any]    	=  (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]		)						,       [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]						,       )







							def 					snake_case	(					self  :     List[str]		):
										def check_hidden_states_output(__snake_case  :     Optional[Any]						,       __snake_case  :     str						,       __snake_case  :     Dict		):
													lowerCamelCase    :List[str]    	=  model_class(__snake_case		)
													model.to(__snake_case		)
													model.eval()

													with torch.no_grad():
																lowerCamelCase    :Optional[int]    	=  model(**self._prepare_for_class(__snake_case						,       __snake_case		)		)

													lowerCamelCase    :List[str]    	=  outputs.hidden_states

													lowerCamelCase    :List[str]    	=  self.model_tester.num_encoder_blocks
													self.assertEqual(len(__snake_case		)						,       __snake_case		)

													# verify the first hidden states (first block)
													self.assertListEqual(
													    list(hidden_states[0].shape[-3:]		)						,       [
													        self.model_tester.hidden_sizes[0],
													        self.model_tester.image_size // 4,
													        self.model_tester.image_size // 4,
													    ]						,       )

										lowerCamelCase    :Dict    	=  self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase    :Any    	=  True
													check_hidden_states_output(__snake_case						,       __snake_case						,       __snake_case		)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													lowerCamelCase    :Dict    	=  True

													check_hidden_states_output(__snake_case						,       __snake_case						,       __snake_case		)







							def 					snake_case	(					self  :     Dict		):
										if not self.model_tester.is_training:
													return

										lowerCamelCase    :List[str]    	=  self.model_tester.prepare_config_and_inputs_for_common()
										lowerCamelCase    :List[Any]    	=  True

										for model_class in self.all_model_classes:
													if model_class in get_values(__snake_case		):
																continue

													lowerCamelCase    :Any    	=  model_class(__snake_case		)
													model.to(__snake_case		)
													model.train()
													lowerCamelCase    :Optional[Any]    	=  self._prepare_for_class(__snake_case						,       __snake_case						,       return_labels=__snake_case		)
													lowerCamelCase    :str    	=  model(**__snake_case		).loss
													loss.backward()







							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''		)
							def 					snake_case	(					self  :     Dict		):
										pass







							@slow
							def 					snake_case	(					self  :     Tuple		):
										for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase    :Union[str, Any]    	=  SegformerModel.from_pretrained(__snake_case		)
													self.assertIsNotNone(__snake_case		)





def 			_lowerCamelCase       ( ):
			lowerCamelCase    :Optional[Any]    	=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
			return image


@require_torch
class 	_lowerCAmelCase		(  unittest.TestCase					):







							@slow
							def 					snake_case	(					self  :     Optional[int]		):
										# only resize + normalize
										lowerCamelCase    :List[Any]    	=  SegformerImageProcessor(
										    image_scale=(512, 512)						,       keep_ratio=__snake_case						,       align=__snake_case						,       do_random_crop=__snake_case		)
										lowerCamelCase    :Optional[Any]    	=  SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512'''		).to(
										    __snake_case		)

										lowerCamelCase    :Tuple    	=  prepare_img()
										lowerCamelCase    :Tuple    	=  image_processor(images=__snake_case						,       return_tensors='''pt'''		)
										lowerCamelCase    :int    	=  encoded_inputs.pixel_values.to(__snake_case		)

										with torch.no_grad():
													lowerCamelCase    :Union[str, Any]    	=  model(__snake_case		)

										lowerCamelCase    :List[str]    	=  torch.Size((1, model.config.num_labels, 128, 128)		)
										self.assertEqual(outputs.logits.shape						,       __snake_case		)

										lowerCamelCase    :str    	=  torch.tensor(
										    [
										        [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
										        [[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
										        [[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
										    ]		).to(__snake_case		)
										self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]						,       __snake_case						,       atol=1e-4		)		)







							@slow
							def 					snake_case	(					self  :     str		):
										# only resize + normalize
										lowerCamelCase    :List[str]    	=  SegformerImageProcessor(
										    image_scale=(512, 512)						,       keep_ratio=__snake_case						,       align=__snake_case						,       do_random_crop=__snake_case		)
										lowerCamelCase    :Any    	=  SegformerForSemanticSegmentation.from_pretrained(
										    '''nvidia/segformer-b1-finetuned-cityscapes-1024-1024'''		).to(__snake_case		)

										lowerCamelCase    :str    	=  prepare_img()
										lowerCamelCase    :Tuple    	=  image_processor(images=__snake_case						,       return_tensors='''pt'''		)
										lowerCamelCase    :Optional[int]    	=  encoded_inputs.pixel_values.to(__snake_case		)

										with torch.no_grad():
													lowerCamelCase    :List[str]    	=  model(__snake_case		)

										lowerCamelCase    :str    	=  torch.Size((1, model.config.num_labels, 128, 128)		)
										self.assertEqual(outputs.logits.shape						,       __snake_case		)

										lowerCamelCase    :List[str]    	=  torch.tensor(
										    [
										        [[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
										        [[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
										        [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
										    ]		).to(__snake_case		)
										self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]						,       __snake_case						,       atol=1e-1		)		)







							@slow
							def 					snake_case	(					self  :     Optional[Any]		):
										# only resize + normalize
										lowerCamelCase    :int    	=  SegformerImageProcessor(
										    image_scale=(512, 512)						,       keep_ratio=__snake_case						,       align=__snake_case						,       do_random_crop=__snake_case		)
										lowerCamelCase    :Any    	=  SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512'''		).to(
										    __snake_case		)

										lowerCamelCase    :Dict    	=  prepare_img()
										lowerCamelCase    :Tuple    	=  image_processor(images=__snake_case						,       return_tensors='''pt'''		)
										lowerCamelCase    :Any    	=  encoded_inputs.pixel_values.to(__snake_case		)

										with torch.no_grad():
													lowerCamelCase    :Optional[int]    	=  model(__snake_case		)

										lowerCamelCase    :Optional[int]    	=  outputs.logits.detach().cpu()

										lowerCamelCase    :Optional[Any]    	=  image_processor.post_process_semantic_segmentation(outputs=__snake_case						,       target_sizes=[(500, 300)]		)
										lowerCamelCase    :str    	=  torch.Size((500, 300)		)
										self.assertEqual(segmentation[0].shape						,       __snake_case		)

										lowerCamelCase    :List[Any]    	=  image_processor.post_process_semantic_segmentation(outputs=__snake_case		)
										lowerCamelCase    :int    	=  torch.Size((128, 128)		)
										self.assertEqual(segmentation[0].shape						,       __snake_case		)








'''simple docstring'''






import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler

class 				a__:







    def __init__( self					:    List[Any]		,     __snake_case					:    str      ):
      if isinstance(__snake_case		,     __snake_case      ):
        # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
        # modified it, it will not be accepted here again, since `auto` values would have been overridden
        a		:   Union[str, Any]	  =	deepcopy(__snake_case      )
      elif os.path.exists(__snake_case      ):
        with io.open(__snake_case		,     'r'		,     encoding='utf-8'      ) as f:
          a		:   List[str]	  =	json.load(__snake_case      )
      else:
        try:
          a		:   str	  =	baseaa.urlsafe_baadecode(__snake_case      ).decode('utf-8'      )
          a		:   Union[str, Any]	  =	json.loads(__snake_case      )
        except (UnicodeDecodeError, AttributeError, ValueError):
          raise ValueError(
              F"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}"""      )

      a		:   Optional[int]	  =	config

      self.set_stage_and_offload()







    def      lowercase_			( self					:    Any      ):
      # zero stage - this is done as early as possible, before model is created, to allow
      # ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
      # during ``zero.Init()`` which needs to know the dtype, and some other hparams.
      a		:   List[str]	  =	self.get_value('zero_optimization.stage'		,     -1      )

      # offload
      a		:   Union[str, Any]	  =	False
      if self.is_zeroa() or self.is_zeroa():
        a		:   Any	  =	set(['cpu', 'nvme']      )
        a		:   Tuple	  =	set(
            [
                self.get_value('zero_optimization.offload_optimizer.device'      ),
                self.get_value('zero_optimization.offload_param.device'      ),
            ]      )
        if len(offload_devices & offload_devices_valid      ) > 0:
          a		:   Optional[int]	  =	True







    def      lowercase_			( self					:    str		,     __snake_case					:    Optional[Any]      ):
      a		:   Any	  =	self.config

      # find the config node of interest if it exists
      a		:   Dict	  =	ds_key_long.split('.'      )
      a		:   str	  =	nodes.pop()
      for node in nodes:
        a		:   Tuple	  =	config.get(__snake_case      )
        if config is None:
          return None, ds_key

      return config, ds_key







    def      lowercase_			( self					:    int		,     __snake_case					:    Optional[int]		,     __snake_case					:    List[str]=None      ):
      a  ,      a		:   int	  =	self.find_config_node(__snake_case      )
      if config is None:
        return default
      return config.get(__snake_case		,     __snake_case      )







    def      lowercase_			( self					:    Dict		,     __snake_case					:    List[str]		,     __snake_case					:    List[Any]=False      ):
      a		:   Union[str, Any]	  =	self.config

      # find the config node of interest if it exists
      a		:   Tuple	  =	ds_key_long.split('.'      )
      for node in nodes:
        a		:   Optional[int]	  =	config
        a		:   List[str]	  =	config.get(__snake_case      )
        if config is None:
          if must_exist:
            raise ValueError(F"""Can't find {ds_key_long} entry in the config: {self.config}"""      )
          else:
            return

        # if found remove it
      if parent_config is not None:
        parent_config.pop(__snake_case      )







    def      lowercase_			( self					:    List[str]		,     __snake_case					:    Dict      ):
      a		:   Optional[Any]	  =	self.get_value(__snake_case      )
      return False if value is None else bool(__snake_case      )







    def      lowercase_			( self					:    str		,     __snake_case					:    Optional[Any]      ):
      a		:   Optional[Any]	  =	self.get_value(__snake_case      )
      return False if value is None else not bool(__snake_case      )







    def      lowercase_			( self					:    Tuple      ):
      return self._stage == 2







    def      lowercase_			( self					:    str      ):
      return self._stage == 3







    def      lowercase_			( self					:    int      ):
      return self._offload

class 				a__:







    def __init__( self					:    str		,     __snake_case					:    Any      ):
      a		:   Any	  =	engine







    def      lowercase_			( self					:    Optional[Any]		,     __snake_case					:    int		,     **__snake_case					:    List[Any]      ):
      # runs backpropagation and handles mixed precision
      self.engine.backward(__snake_case		,     **__snake_case      )

      # Deepspeed's `engine.step` performs the following operations:
      # - gradient accumulation check
      # - gradient clipping
      # - optimizer step
      # - zero grad
      # - checking overflow
      # - lr_scheduler step (only if engine.lr_scheduler is not None)
      self.engine.step()
      # and this plugin overrides the above calls with no-ops when Accelerate runs under
      # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
      # training loop that works transparently under many training regimes.

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    Dict		,     __snake_case					:    Any      ):
      super().__init__(__snake_case		,     device_placement=__snake_case		,     scaler=__snake_case      )
      a		:   List[Any]	  =	hasattr(self.optimizer		,     'overflow'      )







    def      lowercase_			( self					:    Optional[Any]		,     __snake_case					:    str=None      ):
      pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







    def      lowercase_			( self					:    int      ):
      pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







    @property
    def      lowercase_			( self					:    Any      ):
      if self.__has_overflow__:
        return self.optimizer.overflow
      return False

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    str		,     __snake_case					:    List[Any]		,     __snake_case					:    str      ):
      super().__init__(__snake_case		,     __snake_case      )







    def      lowercase_			( self					:    Union[str, Any]      ):
      pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

class 				a__:







    def __init__( self					:    Optional[Any]		,     __snake_case					:    Union[str, Any]		,     __snake_case					:    int=0.001		,     __snake_case					:    Any=0		,     **__snake_case					:    List[Any]      ):
      a		:   Optional[Any]	  =	params
      a		:   Union[str, Any]	  =	lr
      a		:   Any	  =	weight_decay
      a		:   Any	  =	kwargs

class 				a__:







    def __init__( self					:    Any		,     __snake_case					:    int		,     __snake_case					:    int=None		,     __snake_case					:    Dict=0		,     **__snake_case					:    Optional[int]      ):
      a		:   Optional[int]	  =	optimizer
      a		:   List[str]	  =	total_num_steps
      a		:   Tuple	  =	warmup_num_steps
      a		:   Optional[Any]	  =	kwargs